Who's Who of AI

Robert Hawkins

246 trust researcher @rdhawkins.bsky.social Β· 3,990 followers
NLP & language AI business AI research
Why they matter

Researcher with public evidence across NLP & language, AI business, AI research.

AI signals
58
past 30d
Sources
3
distinct domains
Discussions
1
past 30d
Latest signal
1d ago
View every signal from Robert Hawkins β†’
asst prof @Stanford linguistics | director of social interaction lab 🌱 | bluskies about computational cognitive science & language
What they're sharing

Articles & links

↻ Robert Hawkins reposted
Sung Kim @sungkim.bsky.social

Mathematics in the age of AI by Terence Tao An essay on how the mathematical community might respond to the arrival of AI tools that are capable of performing research-level mathematical tasks. arxiv.org/abs/2608.16753

Mathematics in the age of AI arxiv.org
AI Weekly's analysis β†’
  • Terence Tao's ICM 2026 essay sidesteps the debate over AI's math capability and focuses on how results are verified, communicated and digested by the community.
  • In the First Proof evaluation Tao cites, seven of ten novel problems got at least one passing grade from an AI system, at tens to hundreds of dollars each.
  • Tao would block publication if authors cannot give a clear, expert-level talk on their own AI-assisted result, and requires disclosure of any tool use.
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AI agents are checking the scientific literature and spotting decades-old errors www.nature.com/articles/d41...

AI agents are checking the scientific literature β€” and spotting decades-old errors nature.com
AI Weekly's analysis β†’
  • A Zhejiang Lab chemist's AI predicting boiling points clashed with a 75-year-old reference database; manual checks showed the database, not the model, was wrong.
  • The same AI spotted further mistakes in older papers and reference books, including a typo and incorrect values of century-old boiling-point measurements.
  • Researchers caution AI fact-checkers are not reliable on their own because the models make mistakes like humans do and still need manual oversight.
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↻ Robert Hawkins reposted
Russ Poldrack @russpoldrack.org

Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis arxiv.org/abs/2608.19902 - our group's latest work, led by Zijiao Chen, on an agentic harness for neuroimaging analysis that aims to promote analytic rigor.

Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis arxiv.org
AI Weekly's analysis β†’
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↻ Robert Hawkins reposted
Eugene Vinitsky @eugenevinitsky.bsky.social

"LLMs are just n-dimensional classifiers" has a very similar flavor to "everything is just quantum mechanical interactions." www.science.org/doi/10.1126/...

science.org View on Bluesky β†’
↻ Robert Hawkins reposted
suhr @suhr.bsky.social

Sharing this really cool paper of ours that will appear at EMNLP in November: arxiv.org/abs/2606.319...

DigitalCoach: Communication and Grounding Gaps in Human and Agentic Computer Use Coaching arxiv.org
AI Weekly's analysis β†’
  • The DigitalCoach dataset captures 72 expert-novice sessions, 22,752 dialogue turns and 28.1 hours of screen and input recordings across five software applications.
  • Automated evaluation finds models give more direct instructions but fewer explanations, error diagnoses and knowledge-check questions than human coaches.
  • Even when coaching method is standardized, model utterances remain poorly grounded in visual context and learners fall into passive instruction-following.
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↻ Robert Hawkins reposted
Grzegorz ChrupaΕ‚a @grzegorz.chrupala.me

Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe At #EMNLP2026 @emnlpmeeting.bsky.social, @gaofeishen.com presents our poster tomorrow (Sun 25 Oct), 16:00–17:30, Hall H, Poster Session B. πŸ“„ https://aclanthology.org/2026.emnlp-main.654/

Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe aclanthology.org
AI Weekly's analysis β†’
  • The paper argues standard decoding probes can't directly compare feature contributions and are distorted by feature correlations.
  • The Encoding Probe reverses direction and reconstructs transformer internal states from interpretable features spanning acoustics, phonetics, syntax, lexicon and speaker identity.
  • Speaker-related effects vary strongly across training objectives and datasets, while syntactic and lexical features contribute independently to reconstruction.
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↻ Robert Hawkins reposted
@gutlin.bsky.social

Large language models for computational social science www.nature.com/articles/s41...

Large language models for computational social science - Nature Human Behaviour nature.com
AI Weekly's analysis β†’
  • The review groups LLM uses in social science into four roles: simulators of human behaviour, analysts, hypothesis-generators and quality evaluators.
  • When given demographic or ideological personas, models reproduce survey distributions, economic games and polarization dynamics, the authors report.
  • The authors warn LLMs struggle with causal inference and that their ability to reproduce social complexity remains uneven and unproven.
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↻ Robert Hawkins reposted
@gutlin.bsky.social

Large language models for computational social science www.nature.com/articles/s41...

Large language models for computational social science - Nature Human Behaviour nature.com
AI Weekly's analysis β†’
  • The review identifies four roles for LLMs: subjects, assistants, collaborators and evaluators spanning sociology, political science, psychology, anthropology and economics.
  • The authors say LLMs 'struggle with causal inference' and their 'ability to reproduce social complexity remains uneven and unproven.'
  • The paper frames LLMs as both research tools and authentic research subjects as AI agents saturate society.
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